• Vedant Balasaheb Wagh
• Dr.R.S.Khule
• Pooja Kale
• Ketan Deore
• Sumeet Patil

Traditional malware detection struggles against obfuscated malware, where attackers deliberately camouflage malicious code. To address this, a novel obfuscated malware detection system is proposed. This system utilizes a multi-layered approach, starting with static analysis to extract features like code structure, function calls, and even signs of obfuscation techniques themselves. These features are then fed into a machine learning model trained to distinguish malicious patterns from benign software. The system can optionally include dynamic analysis, monitoring the program's runtime behavior for suspicious actions like system calls or memory access patterns. This multi-layered approach offers several advantages. It bypasses limitations of signature-based detection by analyzing obfuscated code, adapts to new obfuscation techniques through machine learning, and scales efficiently for large datasets. The system can be further enhanced with deep learning and threat intelligence integration for continuous improvement. By combining static analysis, machine learning, and optional dynamic analysis, this system offers a promising approach to combat obfuscated malware and improve overall system security.

Obfuscated Malware, Machine Learning, Static Analysis (Optional: Static Code Analysis), Dynamic Analysis, Pattern Recognition, Malware Detection, Cybersecurity, Evasion Techniques

"Obfuscated Malware Detection System to Evaluating Robustness of Learning based Malware Detection System using a Obfuscation Dataset. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k191-k194, April-2024, Available :http://www.jetir.org/papers/JETIR2404A25.pdf

"Obfuscated Malware Detection System to Evaluating Robustness of Learning based Malware Detection System using a Obfuscation Dataset. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk191-k194, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A25.pdf